Prediction model of coal seam gas content based on ACSOA optimized BP neural network

• Published in: Mei kuang an quan Vol. 53; no. 1; pp. 174 - 180
• Main Author: Prediction model of coal seam gas content based on ACSOA optimized BP neural network
• Format: Journal Article
• Language: Chinese
• Published: Editorial Office of Safety in Coal Mines 01.01.2022
• Subjects: bp neural network; chaos theory; gas content; prediction model; seagull optimization algorithm
• ISSN: 1003-496X
• DOI: 10.13347/j.cnki.mkaq.2022.01.028

For the problem of coal seam gas content prediction, the influencing factors of coal seam gas content were analyzed by taking No.2 coal seam of Chensilou Coal Mine as the research object. Based on the above, a prediction model of coal seam gas content was proposed based on adaptive chaotic seagull optimization algorithm (ACSOA) optimized BP neural network (ACSOA-BP). In the ACSOA, introducing chaos algorithm into SOA algorithm for chaos initialization, and adaptive algorithm and nonlinear convergence factor was proposed in SOA algorithm to improve the optimization ability. And the ACSOA-BP model was applied to the study area to verification. The results show that the relationship is nonlinear between gas content of No.2 coal seam and the influencing factors in Chensilou Coal Mine, and the geological structure is the main controlling factor of gas distribution. Compared with the BP model and the SOA-BP model, the ACSOA-BP model has a higher accuracy and stability.